Predictive Analytics with Microsoft Azure Machine Learning, 2/e (Paperback)

Valentine Fontama

Predictive Analytics with Microsoft Azure Machine Learning, 2/e (Paperback)

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商品描述

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models.

The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applying machine learning, evaluating the models, and deploying them as web services.

Learn how you can quickly build and deploy sophisticated predictive models with the new Azure Machine Learning from Microsoft.

What’s New in the Second Edition?

Five new chapters have been added with practical detailed coverage of:

  • Python Integration – a new feature announced February 2015
  • Data preparation and feature selection
  • Data visualization with Power BI
  • Recommendation engines
  • Selling your models on Azure Marketplace

What you’ll learn

  • A structured introduction to Data Science and its best practices
  • An introduction to the new Microsoft Azure Machine Learning service, explaining how to effectively build and deploy predictive models
  • Practical skills such as how to solve typical predictive analytics problems like propensity modeling, churn analysis, product recommendation, and visualization with Power BI
  • A practical way to sell your own predictive models on the Azure Marketplace

Who this book is for

Data Scientists, Business Analysts, BI Professionals and Developers who are interested in expanding their repertoire of skill applied to machine learning and predictive analytics, as well as anyone interested in an in-depth explanation of the Microsoft Azure Machine Learning service through practical tasks and concrete applications.

The reader is assumed to have basic knowledge of statistics and data analysis, but not deep experience in data science or data mining. Advanced programming skills are not required, although some experience with R programming would prove very useful.

Table of Contents

Part 1: Introducing Data Science and Microsoft Azure Machine Learning

1. Introduction to Data Science

2. Introducing Microsoft Azure Machine Learning

3. Data Preparation

4. Integration with R

Part 2: Statistical and Machine Learning Algorithms

5. Integration with Python

Part 3: Practical applications

6. Introduction to Statistical and Machine Learning Algorithms

7. Building Customer Propensity Models

8. Visualizing Your Models with Power BI

9. Building Churn Models

10. Customer Segmentation Models

11. Building Predictive Maintenance Models

12. Recommendation Systems

13. Consuming and Publishing Models on Azure Marketplace

14. Cortana Analytics

商品描述(中文翻譯)

《使用 Microsoft Azure Machine Learning 进行预测分析，第二版》是一本实用的教程，介绍了数据科学和机器学习领域，重点是构建和部署预测模型。该书全面介绍了于2015年2月18日正式发布的 Microsoft Azure Machine Learning 服务，并提供了构建推荐系统、倾向模型、流失和预测维护模型的实用指导。

作者使用任务导向的描述和具体的端到端示例，确保读者可以立即开始使用这项新服务。本书描述了从数据输入到应用机器学习、评估模型和部署模型为 Web 服务的服务的所有方面。

学习如何使用 Microsoft 的新 Azure Machine Learning 快速构建和部署复杂的预测模型。

第二版的新增内容包括五个新章节，详细介绍了以下内容：

- Python 集成 - 2015年2月宣布的新功能
- 数据准备和特征选择
- 使用 Power BI 进行数据可视化
- 推荐引擎
- 在 Azure Marketplace 上销售您的模型

您将学到什么：

- 数据科学及其最佳实践的结构化介绍
- 新的 Microsoft Azure Machine Learning 服务的介绍，解释如何有效地构建和部署预测模型
- 实际技能，如如何解决典型的预测分析问题，如倾向建模、流失分析、产品推荐和使用 Power BI 进行可视化
- 在 Azure Marketplace 上销售自己的预测模型的实际方法

本书适合的读者：

- 数据科学家、业务分析师、BI 专业人员和开发人员，他们有兴趣扩展应用于机器学习和预测分析的技能，并对 Microsoft Azure Machine Learning 服务通过实际任务和具体应用有深入的解释感兴趣。
- 读者应具备基本的统计和数据分析知识，但不需要深入的数据科学或数据挖掘经验。不需要高级编程技能，尽管一些 R 编程经验会非常有用。

目录：

第一部分：介绍数据科学和 Microsoft Azure Machine Learning
1. 数据科学简介
2. 介绍 Microsoft Azure Machine Learning
3. 数据准备
4. 与 R 集成

第二部分：统计和机器学习算法
5. 与 Python 集成

第三部分：实际应用
6. 统计和机器学习算法简介
7. 构建客户倾向模型
8. 使用 Power BI 可视化模型
9. 构建流失模型
10. 客户分割模型
11. 构建预测维护模型
12. 推荐系统
13. 在 Azure Marketplace 上使用和发布模型
14. Cortana Analytics

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